Resource Allocation and Pricing in Secondary Dynamic Spectrum Access Networks

The paradigm shift from static spectrum allocation to a dynamic one has opened many challenges that need to be addressed for the true vision of Dynamic Spectrum Access (DSA) to materialize. This dissertation proposes novel solutions that include: spectrum allocation, routing, and scheduling in DSA networks. First, we propose an auction-based spectrum allocation scheme in a multi-channel environment where secondary users (SUs) bid to buy channels from primary users (PUs) based on the signal to interference and noise ratio (SINR). The channels are allocated such that i) the SUs get their preferred channels, ii) channels are re-used, and iii) there is no interference. Then, we propose a double auction-based spectrum allocation technique by considering multiple bids from SUs and heterogeneity of channels. We use virtual grouping of conflict-free buyers to transform multi-unit bids to single-unit bids. For routing, we propose a market-based model where the PUs determine the optimal price based on the demand for bandwidth by the SUs. Routes are determined through a series of price evaluations between message senders and forwarders. Also, we consider auction-based routing for two cases where buyers can bid for only one channel or they could bid for a combination of non-substitutable channels. For a centralized DSA, we propose two scheduling algorithms-- the first one focuses on maximizing the throughput and the second one focuses on fairness. We extend the scheduling algorithms to multi-channel environment. Expected throughput for every channel is computed by modelling channel state transitions using a discrete-time Markov chain. The state transition probabilities are calculated which occur at the frame/slot boundaries. All proposed algorithms are validated using simulation experiments with different network settings and their performance are studied

Multi-Bid Auctions For Channel Allocation In Multi-Channel Dynamic Spectrum Access Networks

In this paper, we propose an auction-based spectrum allocation scheme in a multi-channel distributed cognitive radio network where primary users are the sellers and secondary users are the buyers. The SUs bid for the various channels that the primaries want to sell; however their bids based on the preference for the channels. We use the signal to interference and noise ratio (SINR) as a metric for the preference; bids for channels with higher SINR are more than the ones that have low SINR. The proposed auction scheme allocates the channels such that the high-preference channels are allocated to the SUs-which does not necessarily maximize revenue for the PUs. The allocation of channels are done such that the i) SUs get their preferred channel, ii) channels are spatially re-used, and iii) a channel is not used by any interfering primary or secondary users. We use the interference conflict graph and the preference list as inputs to the auction-based allocation process. We validate the proposed allocation process through simulation experiments and show what fraction of secondary users get the channels and what the preference was for the assigned channel. Moreover, we measure the effect of the transmission range on the number of assigned channels. We also show that the proposed scheme is fair using Jain\u27s fairness index

Combinatorial Auction Based Routing In Multi-Channel Cognitive Radio Networks

In this paper, we propose an auction-based routing algorithm in a distributed cognitive radio network. The routing of packets is viewed as a market-based phenomenon where channels are traded between transmitters and receivers. Message routes are determined through a series of auctions between message senders and forwarders. The use of auction facilitates the determination of the cost of bandwidth in a market-like scenario, hence the price to be paid by the senders, for each of the possible routes. The process involves the forwarders (or, bidders) bidding the price and the sender choosing the winner with the minimum bid. While the proposed schema explores all routes from the source to the destination through a repeated bidding process, the route with the minimum payment is chosen as the optimal solution that satisfies the requirement of the sender. Bidders determine their bid based on three utility values: i) signal to interference and noise ratio on a specific channel, ii) achievable capacity on each channel, and iii) the required bandwidth to satisfy the buyer\u27s bit rate requirement. We consider two cases: i) sellers can bid for only one channel; in this case the bid is for the channel which satisfies the bit rate requirement and has the minimum price and ii) sellers can bid for a combination of non-substitutable channels that collectively satisfy the bit rate requirement. Since exploring all possible combinations of channels is NR-hard, we propose a greedy heuristic that takes O(nlogn). We validate the proposed auction based routing through extensive simulation experiments. Results are obtained for different topologies and for various number of channels and networking parameters

Queue Based Scheduling In Single And Multi Channel Dynamic Spectrum Access Networks

One of the ways to harness the radio bands that are not used by their primary owners, is to allow opportunistic use of the bands by secondary users (SUs) such that they do not interfere with the primary users. In a dynamic spectrum access (DSA) network, multiple SUs contend to acquire the bands that are available and proper scheduling techniques can resolve such contentions. However, all scheduling techniques have their own pros and cons. In this paper, we propose scheduling algorithms with different objectives for secondary users in a DSA network. Scheduling is performed at the beginning of each super-frame where appropriate channel(s) are assigned to the SUs based on the expected throughput for each channel on specific time slots. Throughput is computed considering: (i) primary channel occupancy, (ii) signal-to-interference-and-noise ratio, and (iii) back-logged queue length of the SUs. We consider two DSA environments: single channel and multi-channel. We propose two scheduling algorithms—the first one focuses on maximizing the expected throughput and the second one focuses on fairness. We model the state of the channel for every user using a discrete-time Markov chain (DTMC) and compute their state transition probabilities. Multiple SUs are allocated the same channel on the same time slot as long as there is no interference between them. Such non-interfering sets of SUs are found by considering the expected throughput of each user on all candidate channels. In order to maximize spatial and temporal reuse of channels, the non-interfering sets are assigned as many channels as possible. Moreover, the channel is reused among the non-interfering sets in the same super-frame. Performances of the proposed scheduling schemes are validated using simulations where we measure metrics such as throughput, number of slots allocated, fairness, delay, and blocking probability. We also show the efficiency of the queue-aware scheduling by comparing with one that does not consider the SU queues

Preda: Preference-Based Double Auction For Heterogeneous Multi-Channel Dsa Networks

In this paper, we propose PreDA-a preferencebased truthful double auction for dynamic spectrum access (DSA) networks where multiple heterogeneous spectrum bands are sold by the primary users and bought by the secondary users. We consider channels\u27 heterogeneity and multi-bids from buyers, and also consider buyers\u27 preferences for the channels. We use the signal to interference and noise ratio (SINR) as a metric for the preference; channels with higher SINR are preferred and hence bids are more compared to the bids for channels that offer lower SINRs. In order to maximize spatial and temporal reuse of channels, we use the concept of virtual grouping of conflict-free buyers. Virtual groups allow us to transform multi-unit bids to single-unit bids. We propose a novel winner determination and pricing mechanism to allocate the unused spectrum bands to the most appropriate buyers. We validate PreDA through simulation experiments and show its performance in terms of the number of allocated bands, utilization, revenue, and fairness

Scheduling In Dynamic Spectrum Access Networks: Throughput And Fairness Tradeoffs

One of the ways to harness the radio bands that are not used by their primary owners, is to allow opportunistic use of the bands to be used by secondary users such that they do not interfere with the primary users. Most often, multiple secondary users contend to acquire the bands that are available. Proper scheduling techniques can resolve such contentions. However, all scheduling techniques have their own pros and cons. In this paper, we propose scheduling algorithms for dynamic spectrum access (DSA) networks that allocate channels by exploiting favorable instantaneous channel conditions of secondary users and the transmission activities of primary users. We propose two scheduling algorithms-the first one focuses on maximizing the throughput and the second one focuses on social welfare. Scheduling is performed at the beginning of each super-frame where appropriate slots(s) are assigned to secondary users. In order to better utilize the channels, multiple secondary users are allocated the same channel in the same time slot as long as there is no interference between them. However, finding such conflict-free independent set of secondary users is a NP-complete problem. We use the signal to interference and noise ratio (SINR) to find the independent sets for throughput maximization, while we use the allocation history for the achieving fairness. Using simulation, we show the performances of the proposed algorithms in terms of throughput, number of slots allocated, and fairness achieved among the users

SCAP SigFox: A Scalable Communication Protocol for Low-Power Wide-Area IoT Networks

The Internet of Things (IoT) is a new future technology that is aimed at connecting billions of physical-world objects to the IT infrastructure via a wireless medium. Many radio access technologies exist, but few address the requirements of IoT applications such as low cost, low energy consumption, and long range. Low-Power wide-area network (LPWAN) technologies, especially SigFox, have a low data rate that makes them suitable for IoT applications, especially since the lower the data rate, the longer the usable distance for the radio link. SigFox technology achieves as a main objective network reliability by striving for the successful delivery of data messages through redundancy. Doing so results in one of the SigFox weaknesses, namely the high collision rate, which questions SigFox scalability. In this work, we aimed at avoiding collisions by changing SigFox’s Aloha-based medium access protocol to TDMA and by using only orthogonal channels while removing redundancy. Consequently, every node sends a single copy of the data message on a given orthogonal channel in a specific time slot. To achieve this, we implemented a slot- and channel-allocation protocol (SCAP) on top of SigFox. In other words, our goal was to improve SigFox’s scalability by implementing two mechanisms: time slot allocation and channel allocation. Performance analysis was conducted on large networks with sizes ranging from 1000 to 10,000 nodes to evaluate both technologies: the original SigFox and SCAP SigFox. The simulation results showed that SCAP SigFox highly reduced the probability of collision and energy consumption when compared to the original SigFox. Additionally, SCAP SigFox had a greater throughput and packet delivery ratio (PDR)

Pricing-based routing in cognitive radio networks

In this paper, we propose a pricing-based routing algorithm in a distributed cognitive radio network. The routing of packets is viewed as a market-based phenomenon where bandwidth is traded between transmitters and receivers. We propose a pricing model using which a seller determines the selling price. We also find the interference that a seller is exposed to since it determines the achievable bit-rate for the bandwidth being sold. As for the buyer, we calculate the bandwidth required from different sellers considering the respective signal to noise ratio. The seller with the minimum price is selected as the next hop relay. Thus, the total price to be paid by a source node is the sum of the prices paid at each hop in the route. Through simulation experiments, we validate the proposed scheme and show the prices for various routes1. © 2013 IEEE

Queue-Aware Opportunistic Scheduling In Multi-Channel Dynamic Spectrum Access Networks

In this paper, we propose an opportunistic scheduling algorithm for secondary users (SUs) in a dynamic spectrum access network. Scheduling is performed at the beginning of each super-frame where appropriate channel(s) are assigned to the SUs based on the expected throughput that each channel provides. We compute the expected throughput for each channel on specific time slots in a super-frame based on three factors: i) primary channel occupancy, ii) signal-to-interference-and-noise ratio, and iii) back-logged queue length of the SUs. We consider scheduling both on a frame-by-frame basis and also on a slot-by-slot basis. The state transitions are modeled using a discrete-time Markov chain (DTMC) process and their probabilities are calculated. The state transitions occur at the frame/slot boundaries. Multiple SUs are allocated the same channel on the same time slot as long as there is no interference between them. Such non-interfering sets of SUs is found by considering the expected throughput of each user on all candidate channels. In order to maximize spatial and temporal reuse of channels, the non-interfering sets are assigned to as many channels as possible on maximum possible time slots. Performance of the proposed scheduling schemes is validated using simulations where we measure metrics such as throughput, number of slots allocated, fairness, and blocking probability. We also show the efficacy of the queue-aware scheduling by comparing with one that does not consider the queues

Pricing-Based Routing In Cognitive Radio Networks

In this paper, we propose a pricing-based routing algorithm in a distributed cognitive radio network. The routing of packets is viewed as a market-based phenomenon where bandwidth is traded between transmitters and receivers. We propose a pricing model using which a seller determines the selling price. We also find the interference that a seller is exposed to since it determines the achievable bit-rate for the bandwidth being sold. As for the buyer, we calculate the bandwidth required from different sellers considering the respective signal to noise ratio. The seller with the minimum price is selected as the next hop relay. Thus, the total price to be paid by a source node is the sum of the prices paid at each hop in the route. Through simulation experiments, we validate the proposed scheme and show the prices for various routes1. © 2013 IEEE